Business Intelligence Tools and Integrating

The banking industry has undergone an immense change in the past decade and specifically in terms of operations and service. If we stress upon the banking industry, the apt example is that of ANZ Bank in Australia where the  presence of huge data needs processing and hence, big data is one of the major steps towards the development of data as the banks can ascertain the transaction of the client and its behaviour leading to a boost in the profitability. On the other hand Business intelligence in banking arena has led to trace the revenue system of the individual to ascertain the products or services that are profitable and those not.

The uses of big data analytic in bank can be seen in the presence of SAP that is the biggest platform and the Hana platform has been a major success. Moreover, focussed vendors that use analytical database consists of Actian, Hp vertica, etc. the presence of such tools have aided in the options of high-RAM-to disk ratio with the tools so that specific data can be placed into memory for analysis on an ultra fast mode (Barney & Ray, 2015). The presence of cloud is another important aspect that helps in reaping huge advantage.

Solver’s BI360 is a total BI and corporate performance management that helps in reporting, budgeting and preparation of modules of the dashboard. It helps the data warehouse to combine the sources of data and empower the uses of business with analytical skills for effective decision making. The added benefit of this tool is that the tool grows as per the requirement of the bank and the level of complexity (Junk, 2015). It is easy to use and helps in providing forecasting solution.

The cloud system has led to immense opportunity when it comes to the banking sector. Hence, through the cloud, Big Data provides an innumerable advantage for the bank and the customers.

Fraud is one of the risky events when it comes to the banking industry. The presence of Big Data in the form of cloud ensures that no unauthorized transaction will take place  leading to safety and raises the security of the bank

There are millions of customers having diverse needs and Big Data caters to them in a prompt fashion. Cloud bases analytics have an advantage that they synchronize in real time with the system of big data. This leads to the insight that is actionable and in a dramatic manner. Hence, in this manner, the expansion of the banking industry is possible and leads to more revenue through the process of cost reduction (Chugh & Gandhi, 2013). When the unnecessary costs are removed then the banking industry can provide customers the desired requirement.

With the advent of the system of cloud (Big Data), there will be an automated process, however, the clerical work of the bank officials will go in vain. However, the same official can be kept under the process of the cloud system so that there can be proper compliance and this will ensure a hassle-free service (Chugh & Gandhi, 2013). Constant monitoring and evaluation will help in the proper performance of the task and any glitches can be rectified at the earliest. Therefore, the clerical work will move to automated work, however, to observe the system and happening the same person can be trained and hired.

References

Barney, J. and  Ray, G. (2015) How information technology resources can provide a competitive advantage in customer service. Planning for Information Systems. [online]. 3(2), pp. 444-460. Available from DOI: 10.4236/me.2015.63038 [12 April 2018]

Chugh, R & Gandhi, S. (2013) Why Business Intelligence? Significance of Business Intelligence tools and integrating BI governance with corporate governance. International Journal of E-Entrepreneurship and Innovation [online]. 4(2), pp.1-14. Available from:  https://pdfs.semanticscholar.org/f4e6/d7c508f019de176980a8562dfc5eb3f2190a.pdf [Accessed 12 April 2018]

Junk, D. (2015) Business Intelligence vs Analytics vs Big Data vs Data Mining [online]. Available from: http://blog.apterainc.com/business-intelligence/business-intelligence-vs-analytics-vs-big-data-vs-data-mining [Accessed 12 April 2018]